Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations7327407
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 GiB
Average record size in memory510.7 B

Variable types

DateTime4
Numeric11
Text5
Categorical4

Alerts

order_take_out_type_label is highly imbalanced (60.5%) Imbalance
bill_total_billed is highly skewed (γ1 = 169.5718289) Skewed
bill_total_discount_item_level is highly skewed (γ1 = 116.7694495) Skewed
bill_total_gratuity is highly skewed (γ1 = 501.5964988) Skewed
bill_total_net is highly skewed (γ1 = 157.400082) Skewed
bill_total_tax is highly skewed (γ1 = 305.4724136) Skewed
bill_total_voided is highly skewed (γ1 = 665.3228541) Skewed
payment_amount is highly skewed (γ1 = 1987.839703) Skewed
payment_total_tip is highly skewed (γ1 = 2683.250401) Skewed
sales_revenue_with_tax is highly skewed (γ1 = 169.5704025) Skewed
bill_uuid has unique values Unique
bill_total_billed has 192204 (2.6%) zeros Zeros
bill_total_discount_item_level has 6770774 (92.4%) zeros Zeros
bill_total_gratuity has 7157347 (97.7%) zeros Zeros
bill_total_net has 192460 (2.6%) zeros Zeros
bill_total_tax has 1056107 (14.4%) zeros Zeros
bill_total_voided has 7133326 (97.4%) zeros Zeros
payment_amount has 193161 (2.6%) zeros Zeros
payment_count has 179184 (2.4%) zeros Zeros
payment_total_tip has 4276747 (58.4%) zeros Zeros
sales_revenue_with_tax has 192212 (2.6%) zeros Zeros

Reproduction

Analysis started2025-02-12 23:20:08.063792
Analysis finished2025-02-12 23:36:24.390803
Duration16 minutes and 16.33 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct5112956
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
Minimum2024-07-01 00:00:01
Maximum2025-01-01 21:41:43
Invalid dates0
Invalid dates (%)0.0%
2025-02-12T18:36:24.597644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:36:24.708694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

bill_total_billed
Real number (ℝ)

Skewed  Zeros 

Distinct48338
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.28303
Minimum-4712.1
Maximum74928.61
Zeros192204
Zeros (%)2.6%
Negative12604
Negative (%)0.2%
Memory size111.8 MiB
2025-02-12T18:36:24.813385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4712.1
5-th percentile2.89
Q110.32
median22.01
Q344.69
95-th percentile108.55
Maximum74928.61
Range79640.71
Interquartile range (IQR)34.37

Descriptive statistics

Standard deviation82.053465
Coefficient of variation (CV)2.2614833
Kurtosis107563.33
Mean36.28303
Median Absolute Deviation (MAD)14.46
Skewness169.57183
Sum2.6586053 × 108
Variance6732.7711
MonotonicityNot monotonic
2025-02-12T18:36:24.901169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192204
 
2.6%
15.5 28759
 
0.4%
7 26524
 
0.4%
11.3 26045
 
0.4%
6 24440
 
0.3%
8 23779
 
0.3%
18.9 22187
 
0.3%
12 19656
 
0.3%
4 19487
 
0.3%
6.75 19254
 
0.3%
Other values (48328) 6925072
94.5%
ValueCountFrequency (%)
-4712.1 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2433.2 1
< 0.1%
-1200 1
< 0.1%
-1000 1
< 0.1%
-773.39 1
< 0.1%
-696.03 1
< 0.1%
-651.41 1
< 0.1%
-638.2 1
< 0.1%
ValueCountFrequency (%)
74928.61 1
< 0.1%
33928.25 1
< 0.1%
25131.48 1
< 0.1%
23132.39 1
< 0.1%
22088.31 1
< 0.1%
21000 1
< 0.1%
20551.26 1
< 0.1%
19866 1
< 0.1%
19021.08 1
< 0.1%
16589.81 1
< 0.1%

bill_total_discount_item_level
Real number (ℝ)

Skewed  Zeros 

Distinct18132
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81173694
Minimum-74.3
Maximum4763.45
Zeros6770774
Zeros (%)92.4%
Negative524
Negative (%)< 0.1%
Memory size111.8 MiB
2025-02-12T18:36:24.993439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.3
5-th percentile0
Q10
median0
Q30
95-th percentile2.82
Maximum4763.45
Range4837.75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.4987232
Coefficient of variation (CV)10.4698
Kurtosis37493.172
Mean0.81173694
Median Absolute Deviation (MAD)0
Skewness116.76945
Sum5947926.9
Variance72.228296
MonotonicityNot monotonic
2025-02-12T18:36:25.085519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6770774
92.4%
2 20086
 
0.3%
1 18937
 
0.3%
3 14649
 
0.2%
5 13851
 
0.2%
4 13299
 
0.2%
6 10167
 
0.1%
10 9229
 
0.1%
8 7977
 
0.1%
1.5 6834
 
0.1%
Other values (18122) 441604
 
6.0%
ValueCountFrequency (%)
-74.3 1
< 0.1%
-66.25 1
< 0.1%
-62.84 1
< 0.1%
-59.06 2
< 0.1%
-58.62 2
< 0.1%
-55 1
< 0.1%
-53.6 1
< 0.1%
-53.26 1
< 0.1%
-50.27 1
< 0.1%
-44.04 1
< 0.1%
ValueCountFrequency (%)
4763.45 1
< 0.1%
4152 1
< 0.1%
3624.71 1
< 0.1%
2912.75 1
< 0.1%
2809 1
< 0.1%
2611 1
< 0.1%
2198 1
< 0.1%
2090 1
< 0.1%
2040 1
< 0.1%
1888.8 1
< 0.1%

bill_total_gratuity
Real number (ℝ)

Skewed  Zeros 

Distinct8038
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24172149
Minimum-60
Maximum11935.53
Zeros7157347
Zeros (%)97.7%
Negative24
Negative (%)< 0.1%
Memory size111.8 MiB
2025-02-12T18:36:25.167089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11935.53
Range11995.53
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.722423
Coefficient of variation (CV)36.084598
Kurtosis539453.97
Mean0.24172149
Median Absolute Deviation (MAD)0
Skewness501.5965
Sum1771191.8
Variance76.080663
MonotonicityNot monotonic
2025-02-12T18:36:25.257564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7157347
97.7%
0.32 5362
 
0.1%
0.15 4588
 
0.1%
0.64 2047
 
< 0.1%
0.24 1775
 
< 0.1%
3.6 1367
 
< 0.1%
0.12 1274
 
< 0.1%
0.45 1256
 
< 0.1%
0.3 1253
 
< 0.1%
0.48 1102
 
< 0.1%
Other values (8028) 150036
 
2.0%
ValueCountFrequency (%)
-60 1
< 0.1%
-37.8 1
< 0.1%
-19.08 1
< 0.1%
-18.4 1
< 0.1%
-12 1
< 0.1%
-11.79 1
< 0.1%
-9.58 1
< 0.1%
-3.87 1
< 0.1%
-2.74 1
< 0.1%
-2.44 1
< 0.1%
ValueCountFrequency (%)
11935.53 1
< 0.1%
6005 1
< 0.1%
4029.21 1
< 0.1%
4003.25 1
< 0.1%
3402.7 1
< 0.1%
3245 1
< 0.1%
2916.32 1
< 0.1%
2504.6 1
< 0.1%
2492.6 1
< 0.1%
2469.19 1
< 0.1%

bill_total_net
Real number (ℝ)

Skewed  Zeros 

Distinct36640
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.46403
Minimum-4170
Maximum66308.5
Zeros192460
Zeros (%)2.6%
Negative12449
Negative (%)0.2%
Memory size111.8 MiB
2025-02-12T18:36:25.344276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4170
5-th percentile2.75
Q19.52
median20.24
Q341
95-th percentile100
Maximum66308.5
Range70478.5
Interquartile range (IQR)31.48

Descriptive statistics

Standard deviation76.127465
Coefficient of variation (CV)2.2749043
Kurtosis91094.796
Mean33.46403
Median Absolute Deviation (MAD)13.24
Skewness157.40008
Sum2.4520457 × 108
Variance5795.3909
MonotonicityNot monotonic
2025-02-12T18:36:25.433181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192460
 
2.6%
8 65960
 
0.9%
18 64383
 
0.9%
10 58653
 
0.8%
16 55866
 
0.8%
5 55117
 
0.8%
15 54506
 
0.7%
12 53155
 
0.7%
6 51607
 
0.7%
20 46674
 
0.6%
Other values (36630) 6629026
90.5%
ValueCountFrequency (%)
-4170 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2342.09 1
< 0.1%
-1200 1
< 0.1%
-1000 1
< 0.1%
-684.42 1
< 0.1%
-647.5 1
< 0.1%
-599 1
< 0.1%
-596 1
< 0.1%
ValueCountFrequency (%)
66308.5 1
< 0.1%
30025 1
< 0.1%
22384.5 1
< 0.1%
22240.25 1
< 0.1%
20253.15 1
< 0.1%
20050 1
< 0.1%
19866 1
< 0.1%
18985 1
< 0.1%
17256 1
< 0.1%
15398.9 1
< 0.1%

bill_total_tax
Real number (ℝ)

Skewed  Zeros 

Distinct10043
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.819
Minimum-542.1
Maximum8620.11
Zeros1056107
Zeros (%)14.4%
Negative9274
Negative (%)0.1%
Memory size111.8 MiB
2025-02-12T18:36:25.525195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-542.1
5-th percentile0
Q10.45
median1.43
Q33.49
95-th percentile9.75
Maximum8620.11
Range9162.21
Interquartile range (IQR)3.04

Descriptive statistics

Standard deviation7.1636406
Coefficient of variation (CV)2.5411992
Kurtosis306353.56
Mean2.819
Median Absolute Deviation (MAD)1.23
Skewness305.47241
Sum20655961
Variance51.317747
MonotonicityNot monotonic
2025-02-12T18:36:25.638915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1056107
 
14.4%
1.3 43081
 
0.6%
0.9 39754
 
0.5%
1.04 39536
 
0.5%
0.52 39266
 
0.5%
0.65 38705
 
0.5%
0.24 37644
 
0.5%
0.33 35709
 
0.5%
2.08 35571
 
0.5%
0.85 33725
 
0.5%
Other values (10033) 5928309
80.9%
ValueCountFrequency (%)
-542.1 1
< 0.1%
-91.11 1
< 0.1%
-88.97 1
< 0.1%
-73.85 1
< 0.1%
-73.42 1
< 0.1%
-60.09 1
< 0.1%
-58.71 1
< 0.1%
-52.41 1
< 0.1%
-51.39 1
< 0.1%
-49.93 1
< 0.1%
ValueCountFrequency (%)
8620.11 1
< 0.1%
3903.25 1
< 0.1%
2891.23 1
< 0.1%
2038.31 1
< 0.1%
1765.08 1
< 0.1%
1566.26 1
< 0.1%
1505.53 1
< 0.1%
1334.81 1
< 0.1%
1300 1
< 0.1%
1242.85 1
< 0.1%

bill_total_voided
Real number (ℝ)

Skewed  Zeros 

Distinct9033
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75326427
Minimum-4501.5
Maximum43178.4
Zeros7133326
Zeros (%)97.4%
Negative510
Negative (%)< 0.1%
Memory size111.8 MiB
2025-02-12T18:36:25.762883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4501.5
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43178.4
Range47679.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.828199
Coefficient of variation (CV)36.943474
Kurtosis860737.37
Mean0.75326427
Median Absolute Deviation (MAD)0
Skewness665.32285
Sum5519473.9
Variance774.40866
MonotonicityNot monotonic
2025-02-12T18:36:25.846412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7133326
97.4%
8 4266
 
0.1%
9 3669
 
0.1%
10 3629
 
< 0.1%
16 3601
 
< 0.1%
6 3324
 
< 0.1%
12 3233
 
< 0.1%
5 3229
 
< 0.1%
15 2948
 
< 0.1%
7 2891
 
< 0.1%
Other values (9023) 163291
 
2.2%
ValueCountFrequency (%)
-4501.5 1
< 0.1%
-1697 1
< 0.1%
-1665.9 1
< 0.1%
-1215.15 1
< 0.1%
-1173.15 1
< 0.1%
-784.81 1
< 0.1%
-538.55 1
< 0.1%
-500 1
< 0.1%
-380.75 1
< 0.1%
-313.85 1
< 0.1%
ValueCountFrequency (%)
43178.4 1
< 0.1%
20835 1
< 0.1%
12766.5 1
< 0.1%
12749.46 1
< 0.1%
10176 1
< 0.1%
9440 1
< 0.1%
8216.42 1
< 0.1%
8000 1
< 0.1%
7312.62 1
< 0.1%
6800 1
< 0.1%

bill_uuid
Text

Unique 

Distinct7327407
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size455.0 MiB
2025-02-12T18:36:40.140197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length49
Mean length48.988761
Min length36

Characters and Unicode

Total characters358960588
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7327407 ?
Unique (%)100.0%

Sample

1st row240702200053~8792ADCC-545E-4AF3-9836-9C428ED8285C
2nd row240703214339~B06B2A2F-B1A0-44C0-920A-242F41B58BD2
3rd row240703182356~D082FE98-841C-4EAB-B15E-BEA41FA1CDF0
4th row240703193321~273CBB07-FAB0-49B7-A445-8474F9A4570F
5th row240705210114~D423B906-1AEE-4DB8-84BA-234267A14E05
ValueCountFrequency (%)
240702200053~8792adcc-545e-4af3-9836-9c428ed8285c 1
 
< 0.1%
240703214339~b06b2a2f-b1a0-44c0-920a-242f41b58bd2 1
 
< 0.1%
240703182356~d082fe98-841c-4eab-b15e-bea41fa1cdf0 1
 
< 0.1%
240703193321~273cbb07-fab0-49b7-a445-8474f9a4570f 1
 
< 0.1%
240705210114~d423b906-1aee-4db8-84ba-234267a14e05 1
 
< 0.1%
240705210926~ca81a307-a782-42d4-b81d-250556cbe839 1
 
< 0.1%
240705202148~a47eec00-a5af-45ba-a892-171e7cdefe7c 1
 
< 0.1%
240705182318~913c3f9f-e867-4e36-a3b1-e1e68fcbad16 1
 
< 0.1%
240706174632~1996e368-132e-43a2-9821-34bf48033ce1 1
 
< 0.1%
240706181722~a4851319-949c-4ff1-964d-c860684075b9 1
 
< 0.1%
Other values (7327397) 7327397
> 99.9%
2025-02-12T18:36:50.572387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 33548532
 
9.3%
1 31557189
 
8.8%
2 31504519
 
8.8%
- 29309628
 
8.2%
0 27344136
 
7.6%
9 19845498
 
5.5%
8 19837506
 
5.5%
3 19610787
 
5.5%
5 18804623
 
5.2%
7 17803302
 
5.0%
Other values (14) 109794868
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358960588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 33548532
 
9.3%
1 31557189
 
8.8%
2 31504519
 
8.8%
- 29309628
 
8.2%
0 27344136
 
7.6%
9 19845498
 
5.5%
8 19837506
 
5.5%
3 19610787
 
5.5%
5 18804623
 
5.2%
7 17803302
 
5.0%
Other values (14) 109794868
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358960588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 33548532
 
9.3%
1 31557189
 
8.8%
2 31504519
 
8.8%
- 29309628
 
8.2%
0 27344136
 
7.6%
9 19845498
 
5.5%
8 19837506
 
5.5%
3 19610787
 
5.5%
5 18804623
 
5.2%
7 17803302
 
5.0%
Other values (14) 109794868
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358960588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 33548532
 
9.3%
1 31557189
 
8.8%
2 31504519
 
8.8%
- 29309628
 
8.2%
0 27344136
 
7.6%
9 19845498
 
5.5%
8 19837506
 
5.5%
3 19610787
 
5.5%
5 18804623
 
5.2%
7 17803302
 
5.0%
Other values (14) 109794868
30.6%
Distinct184
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
Minimum2024-07-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-12T18:36:50.654344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:36:50.767489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_duration_seconds
Real number (ℝ)

Distinct65608
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2752.368
Minimum-44
Maximum86397
Zeros122
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size111.8 MiB
2025-02-12T18:36:50.864425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-44
5-th percentile33
Q1106
median1111
Q33290
95-th percentile9088
Maximum86397
Range86441
Interquartile range (IQR)3184

Descriptive statistics

Standard deviation6207.1318
Coefficient of variation (CV)2.2551969
Kurtosis74.744784
Mean2752.368
Median Absolute Deviation (MAD)1060
Skewness7.6162569
Sum2.016772 × 1010
Variance38528485
MonotonicityNot monotonic
2025-02-12T18:36:50.940920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 26486
 
0.4%
43 26436
 
0.4%
46 26321
 
0.4%
45 26242
 
0.4%
40 26155
 
0.4%
44 26104
 
0.4%
47 25904
 
0.4%
41 25902
 
0.4%
38 25863
 
0.4%
48 25762
 
0.4%
Other values (65598) 7066232
96.4%
ValueCountFrequency (%)
-44 1
 
< 0.1%
-19 1
 
< 0.1%
0 122
 
< 0.1%
1 2155
 
< 0.1%
2 5486
0.1%
3 4312
0.1%
4 6022
0.1%
5 6382
0.1%
6 5637
0.1%
7 5224
0.1%
ValueCountFrequency (%)
86397 1
< 0.1%
86396 1
< 0.1%
86394 1
< 0.1%
86392 1
< 0.1%
86391 1
< 0.1%
86387 1
< 0.1%
86384 2
< 0.1%
86383 1
< 0.1%
86382 1
< 0.1%
86381 1
< 0.1%
Distinct4761314
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
Minimum2024-06-30 08:54:47
Maximum2025-01-01 18:57:30
Invalid dates0
Invalid dates (%)0.0%
2025-02-12T18:36:51.023500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:36:51.122327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct5083978
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
Minimum2024-07-01 00:00:01
Maximum2025-01-01 21:41:43
Invalid dates0
Invalid dates (%)0.0%
2025-02-12T18:36:51.216674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:36:51.311312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_take_out_type_label
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
dinein
6010445 
takeout
859617 
onlineorder
 
264578
bartab
 
180981
delivery
 
11786

Length

Max length11
Median length6
Mean length6.3010723
Min length6

Characters and Unicode

Total characters46170521
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdinein
2nd rowdinein
3rd rowdinein
4th rowdinein
5th rowdinein

Common Values

ValueCountFrequency (%)
dinein 6010445
82.0%
takeout 859617
 
11.7%
onlineorder 264578
 
3.6%
bartab 180981
 
2.5%
delivery 11786
 
0.2%

Length

2025-02-12T18:36:51.410264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T18:36:51.478419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dinein 6010445
82.0%
takeout 859617
 
11.7%
onlineorder 264578
 
3.6%
bartab 180981
 
2.5%
delivery 11786
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 12550046
27.2%
i 12297254
26.6%
e 7422790
16.1%
d 6286809
13.6%
t 1900215
 
4.1%
o 1388773
 
3.0%
a 1221579
 
2.6%
k 859617
 
1.9%
u 859617
 
1.9%
r 721923
 
1.6%
Other values (4) 661898
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46170521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 12550046
27.2%
i 12297254
26.6%
e 7422790
16.1%
d 6286809
13.6%
t 1900215
 
4.1%
o 1388773
 
3.0%
a 1221579
 
2.6%
k 859617
 
1.9%
u 859617
 
1.9%
r 721923
 
1.6%
Other values (4) 661898
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46170521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 12550046
27.2%
i 12297254
26.6%
e 7422790
16.1%
d 6286809
13.6%
t 1900215
 
4.1%
o 1388773
 
3.0%
a 1221579
 
2.6%
k 859617
 
1.9%
u 859617
 
1.9%
r 721923
 
1.6%
Other values (4) 661898
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46170521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 12550046
27.2%
i 12297254
26.6%
e 7422790
16.1%
d 6286809
13.6%
t 1900215
 
4.1%
o 1388773
 
3.0%
a 1221579
 
2.6%
k 859617
 
1.9%
u 859617
 
1.9%
r 721923
 
1.6%
Other values (4) 661898
 
1.4%
Distinct6724735
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size455.0 MiB
2025-02-12T18:37:05.915911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length49
Mean length48.988761
Min length36

Characters and Unicode

Total characters358960588
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6384062 ?
Unique (%)87.1%

Sample

1st row240702183622~4B0A3D27-501D-4C94-BEBC-7B7F3C91A0F4
2nd row240703194731~40651D6D-4A16-4889-8C12-0F9A00D8BCF7
3rd row240703175025~0AEB61B0-220A-45A3-807E-78A1F432BB4C
4th row240703180201~4B276496-BE92-4DE6-BD75-565929827014
5th row240705194141~F90FEB4F-D238-449E-B476-D16966DF4EEC
ValueCountFrequency (%)
240914174325~01482558-0947-4341-8662-cf4eb18cc045 52
 
< 0.1%
240913225853~632e2003-bd52-4c2d-a42d-cbe90c35535a 43
 
< 0.1%
241018211013~83c159f1-9998-42ae-b8e2-06154a4a495f 39
 
< 0.1%
240726211619~ce2ffa2c-3bb0-49f9-a695-f3bb0d074c73 38
 
< 0.1%
241012223955~38741a41-132a-4818-a003-c9dc425e4ed0 33
 
< 0.1%
241012194904~25c7b69f-c134-421a-ae23-927c922fa034 32
 
< 0.1%
241108234708~11accc10-5fbc-4559-84f0-73e7a8340afa 31
 
< 0.1%
240920223808~092fe466-3c2a-46ef-8898-684dce87cd8b 30
 
< 0.1%
241130223622~7e197c00-0901-4da1-baf9-b9376c261ef6 30
 
< 0.1%
241206233002~1066d6f0-1fd0-42c2-a919-be9e46763e88 30
 
< 0.1%
Other values (6724725) 7327049
> 99.9%
2025-02-12T18:37:17.224099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 33478345
 
9.3%
1 32005804
 
8.9%
2 31026448
 
8.6%
- 29309628
 
8.2%
0 27181486
 
7.6%
8 19982388
 
5.6%
9 19852290
 
5.5%
3 19500073
 
5.4%
5 18751295
 
5.2%
7 17986919
 
5.0%
Other values (14) 109885912
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358960588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 33478345
 
9.3%
1 32005804
 
8.9%
2 31026448
 
8.6%
- 29309628
 
8.2%
0 27181486
 
7.6%
8 19982388
 
5.6%
9 19852290
 
5.5%
3 19500073
 
5.4%
5 18751295
 
5.2%
7 17986919
 
5.0%
Other values (14) 109885912
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358960588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 33478345
 
9.3%
1 32005804
 
8.9%
2 31026448
 
8.6%
- 29309628
 
8.2%
0 27181486
 
7.6%
8 19982388
 
5.6%
9 19852290
 
5.5%
3 19500073
 
5.4%
5 18751295
 
5.2%
7 17986919
 
5.0%
Other values (14) 109885912
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358960588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 33478345
 
9.3%
1 32005804
 
8.9%
2 31026448
 
8.6%
- 29309628
 
8.2%
0 27181486
 
7.6%
8 19982388
 
5.6%
9 19852290
 
5.5%
3 19500073
 
5.4%
5 18751295
 
5.2%
7 17986919
 
5.0%
Other values (14) 109885912
30.6%

payment_amount
Real number (ℝ)

Skewed  Zeros 

Distinct55550
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.138697
Minimum-4712.1
Maximum522752.27
Zeros193161
Zeros (%)2.6%
Negative12468
Negative (%)0.2%
Memory size111.8 MiB
2025-02-12T18:37:17.319412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4712.1
5-th percentile2.98
Q110.92
median23.65
Q348.9
95-th percentile122.5
Maximum522752.27
Range527464.37
Interquartile range (IQR)37.98

Descriptive statistics

Standard deviation214.56711
Coefficient of variation (CV)5.3456421
Kurtosis4810881.6
Mean40.138697
Median Absolute Deviation (MAD)15.75
Skewness1987.8397
Sum2.9411257 × 108
Variance46039.045
MonotonicityNot monotonic
2025-02-12T18:37:17.429930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193161
 
2.6%
15.5 25022
 
0.3%
7 23095
 
0.3%
6 22025
 
0.3%
11.3 19907
 
0.3%
5 18593
 
0.3%
12 18589
 
0.3%
8 18586
 
0.3%
6.75 18427
 
0.3%
3 18230
 
0.2%
Other values (55540) 6951772
94.9%
ValueCountFrequency (%)
-4712.1 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2433.2 1
< 0.1%
-1200 1
< 0.1%
-1000 1
< 0.1%
-773.39 1
< 0.1%
-696.03 1
< 0.1%
-691.41 1
< 0.1%
-638.2 1
< 0.1%
ValueCountFrequency (%)
522752.27 1
< 0.1%
86864.14 1
< 0.1%
39933.25 1
< 0.1%
29134.73 1
< 0.1%
27161.6 1
< 0.1%
25333.31 1
< 0.1%
25050.63 1
< 0.1%
24348.26 1
< 0.1%
23786.14 1
< 0.1%
22946 1
< 0.1%

payment_count
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9837173
Minimum0
Maximum41
Zeros179184
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size111.8 MiB
2025-02-12T18:37:17.524426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19496385
Coefficient of variation (CV)0.19819093
Kurtosis610.22941
Mean0.9837173
Median Absolute Deviation (MAD)0
Skewness4.9853598
Sum7208097
Variance0.038010902
MonotonicityNot monotonic
2025-02-12T18:37:17.615004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 7098389
96.9%
0 179184
 
2.4%
2 43787
 
0.6%
3 4124
 
0.1%
4 1029
 
< 0.1%
5 470
 
< 0.1%
6 191
 
< 0.1%
7 96
 
< 0.1%
8 46
 
< 0.1%
9 28
 
< 0.1%
Other values (15) 63
 
< 0.1%
ValueCountFrequency (%)
0 179184
 
2.4%
1 7098389
96.9%
2 43787
 
0.6%
3 4124
 
0.1%
4 1029
 
< 0.1%
5 470
 
< 0.1%
6 191
 
< 0.1%
7 96
 
< 0.1%
8 46
 
< 0.1%
9 28
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
22 2
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 2
< 0.1%
17 2
< 0.1%
16 3
< 0.1%

payment_total_tip
Real number (ℝ)

Skewed  Zeros 

Distinct11413
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6178704
Minimum-253.14
Maximum522715.64
Zeros4276747
Zeros (%)58.4%
Negative100
Negative (%)< 0.1%
Memory size111.8 MiB
2025-02-12T18:37:17.702285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-253.14
5-th percentile0
Q10
median0
Q34.2
95-th percentile16.41
Maximum522715.64
Range522968.78
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation193.67719
Coefficient of variation (CV)53.533478
Kurtosis7240831.5
Mean3.6178704
Median Absolute Deviation (MAD)0
Skewness2683.2504
Sum26509609
Variance37510.852
MonotonicityNot monotonic
2025-02-12T18:37:17.806217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4276747
58.4%
5 145569
 
2.0%
2 122759
 
1.7%
10 116203
 
1.6%
1 107670
 
1.5%
3 83271
 
1.1%
4 57886
 
0.8%
20 49432
 
0.7%
6 47655
 
0.7%
8 41089
 
0.6%
Other values (11403) 2279126
31.1%
ValueCountFrequency (%)
-253.14 1
< 0.1%
-207.36 1
< 0.1%
-198.42 1
< 0.1%
-189.5 1
< 0.1%
-179.9 1
< 0.1%
-129.8 1
< 0.1%
-101.7 1
< 0.1%
-99 1
< 0.1%
-83.88 1
< 0.1%
-79.7 1
< 0.1%
ValueCountFrequency (%)
522715.64 1
< 0.1%
23762.38 1
< 0.1%
17668.02 1
< 0.1%
5180.16 1
< 0.1%
4050.63 1
< 0.1%
3797 1
< 0.1%
3080 1
< 0.1%
2714.25 1
< 0.1%
2635.09 1
< 0.1%
2338.94 1
< 0.1%

sales_revenue_with_tax
Real number (ℝ)

Skewed  Zeros 

Distinct48336
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.283892
Minimum-4712.1
Maximum74928.61
Zeros192212
Zeros (%)2.6%
Negative12603
Negative (%)0.2%
Memory size111.8 MiB
2025-02-12T18:37:17.901864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4712.1
5-th percentile2.89
Q110.32
median22.01
Q344.7
95-th percentile108.56
Maximum74928.61
Range79640.71
Interquartile range (IQR)34.38

Descriptive statistics

Standard deviation82.053691
Coefficient of variation (CV)2.2614357
Kurtosis107562.13
Mean36.283892
Median Absolute Deviation (MAD)14.46
Skewness169.5704
Sum2.6586685 × 108
Variance6732.8082
MonotonicityNot monotonic
2025-02-12T18:37:18.006864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192212
 
2.6%
15.5 28759
 
0.4%
7 26524
 
0.4%
11.3 26045
 
0.4%
6 24440
 
0.3%
8 23779
 
0.3%
18.9 22187
 
0.3%
12 19656
 
0.3%
4 19559
 
0.3%
6.75 19254
 
0.3%
Other values (48326) 6924992
94.5%
ValueCountFrequency (%)
-4712.1 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2433.2 1
< 0.1%
-1200 1
< 0.1%
-1000 1
< 0.1%
-773.39 1
< 0.1%
-696.03 1
< 0.1%
-651.41 1
< 0.1%
-638.2 1
< 0.1%
ValueCountFrequency (%)
74928.61 1
< 0.1%
33928.25 1
< 0.1%
25131.48 1
< 0.1%
23132.39 1
< 0.1%
22088.31 1
< 0.1%
21000 1
< 0.1%
20551.26 1
< 0.1%
19866 1
< 0.1%
19021.08 1
< 0.1%
16589.81 1
< 0.1%
Distinct465
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size559.9 MiB
2025-02-12T18:37:18.202337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters468954048
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288
2nd row885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288
3rd row885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288
4th row885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288
5th row885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288
ValueCountFrequency (%)
5e8a21ead84851c212c2cae58849de4d37bc0babfcab05ceff51350412eb3e94 85962
 
1.2%
6c4ddbba39d1529689be33c4769223b1194ba2dcb9e20f266d18face3279d00a 79705
 
1.1%
1fba23fbdd499646399a75417304d971f5fdea72bce5ba4b0d9e6086ac093892 74945
 
1.0%
68e559beb80a2ae3cbdbf07043ebf35fbbccd85494216a2f32b31da76ece5ac0 73329
 
1.0%
5dc459c71f4739c3dbd5872b4d442295864cf5715f704b19e02e1a581453561a 72665
 
1.0%
92128c7fc22f3f685e9914450a0bd855e258aafff9351663260b080a4a7c5188 68486
 
0.9%
6afc5202235f3b742e6482c94536ab8462f6022054eccb0c6d5630c211c0a7fe 66103
 
0.9%
5ece4507d7aec26fdcca66082fcc77457f79cea602c1a82122c7ea496d37c493 65298
 
0.9%
3c5a0e6460177d012e070306ba429335066823de1e25aa1ffbd3df7966cacd81 64713
 
0.9%
7cd024c525a7952ce599081035cf063cb3b0c52626072b2014a79055ef37d670 63627
 
0.9%
Other values (455) 6612574
90.2%
2025-02-12T18:37:18.749865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 32191833
 
6.9%
e 31070515
 
6.6%
c 30968596
 
6.6%
0 29920795
 
6.4%
f 29749227
 
6.3%
b 29595969
 
6.3%
a 29270116
 
6.2%
3 29241722
 
6.2%
6 29107403
 
6.2%
d 28884875
 
6.2%
Other values (6) 168952997
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 468954048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 32191833
 
6.9%
e 31070515
 
6.6%
c 30968596
 
6.6%
0 29920795
 
6.4%
f 29749227
 
6.3%
b 29595969
 
6.3%
a 29270116
 
6.2%
3 29241722
 
6.2%
6 29107403
 
6.2%
d 28884875
 
6.2%
Other values (6) 168952997
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 468954048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 32191833
 
6.9%
e 31070515
 
6.6%
c 30968596
 
6.6%
0 29920795
 
6.4%
f 29749227
 
6.3%
b 29595969
 
6.3%
a 29270116
 
6.2%
3 29241722
 
6.2%
6 29107403
 
6.2%
d 28884875
 
6.2%
Other values (6) 168952997
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 468954048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 32191833
 
6.9%
e 31070515
 
6.6%
c 30968596
 
6.6%
0 29920795
 
6.4%
f 29749227
 
6.3%
b 29595969
 
6.3%
a 29270116
 
6.2%
3 29241722
 
6.2%
6 29107403
 
6.2%
d 28884875
 
6.2%
Other values (6) 168952997
36.0%
Distinct7478
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size441.2 MiB
2025-02-12T18:37:18.950927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length49
Mean length47.017436
Min length20

Characters and Unicode

Total characters344515887
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique255 ?
Unique (%)< 0.1%

Sample

1st row200421150750~37D0C51E-EC4F-4EA1-B549-D223DA183ABD
2nd row220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFA
3rd row220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFA
4th row220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFA
5th row220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFA
ValueCountFrequency (%)
190405092446~8646fe39-600c-4318-bea7-0a31d4082914 384686
 
5.2%
200122200826~9afddbd1-59a4-4016-ad66-830100e21632 43219
 
0.6%
220808130537~4ff8785d-4a7b-49ff-a7fe-b8551b6591d0 38719
 
0.5%
150101000000~99c97c5b-82ae-48b5-a578-cc528ee0c57c 37387
 
0.5%
cc66dfde-4181-46f5-a04d-d14424650700 37364
 
0.5%
201209123916~356b9122-4031-4a51-886f-8dcea9d8afc5 36166
 
0.5%
220528101742~8c27e6e6-4320-45ad-aea9-fdb28a8364cd 31502
 
0.4%
160111173131~2be06004-30a3-4159-8213-0d8ece0549c4 27662
 
0.4%
191105151117~57504afc-9510-479d-bb67-82e999f616c2 26426
 
0.4%
230725153957~09eb7f75-4154-4ec6-83d7-c24ea0ca08ab 24989
 
0.3%
Other values (7468) 6639287
90.6%
2025-02-12T18:37:19.384804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 29416383
 
8.5%
- 29309596
 
8.5%
0 28913626
 
8.4%
1 28490199
 
8.3%
2 27342752
 
7.9%
3 19948474
 
5.8%
9 19157625
 
5.6%
5 18667807
 
5.4%
8 18658318
 
5.4%
6 17348296
 
5.0%
Other values (23) 107262811
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344515887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 29416383
 
8.5%
- 29309596
 
8.5%
0 28913626
 
8.4%
1 28490199
 
8.3%
2 27342752
 
7.9%
3 19948474
 
5.8%
9 19157625
 
5.6%
5 18667807
 
5.4%
8 18658318
 
5.4%
6 17348296
 
5.0%
Other values (23) 107262811
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344515887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 29416383
 
8.5%
- 29309596
 
8.5%
0 28913626
 
8.4%
1 28490199
 
8.3%
2 27342752
 
7.9%
3 19948474
 
5.8%
9 19157625
 
5.6%
5 18667807
 
5.4%
8 18658318
 
5.4%
6 17348296
 
5.0%
Other values (23) 107262811
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344515887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 29416383
 
8.5%
- 29309596
 
8.5%
0 28913626
 
8.4%
1 28490199
 
8.3%
2 27342752
 
7.9%
3 19948474
 
5.8%
9 19157625
 
5.6%
5 18667807
 
5.4%
8 18658318
 
5.4%
6 17348296
 
5.0%
Other values (23) 107262811
31.1%

concept
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
FAMILY_DINING
2531818 
CAFE
1445320 
BAR
1248070 
FAST_CASUAL
933396 
BAKERY
305322 
Other values (9)
863481 

Length

Max length21
Median length13
Mean length8.5454175
Min length3

Characters and Unicode

Total characters62615752
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBAR
2nd rowBAR
3rd rowBAR
4th rowBAR
5th rowBAR

Common Values

ValueCountFrequency (%)
FAMILY_DINING 2531818
34.6%
CAFE 1445320
19.7%
BAR 1248070
17.0%
FAST_CASUAL 933396
 
12.7%
BAKERY 305322
 
4.2%
BREWERY 280258
 
3.8%
FINE_DINING 247651
 
3.4%
FAST_FOOD 191842
 
2.6%
HOTEL 52400
 
0.7%
ENTERTAINMENT_COMPLEX 51919
 
0.7%
Other values (4) 39411
 
0.5%

Length

2025-02-12T18:37:19.468261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
family_dining 2531818
34.6%
cafe 1445320
19.7%
bar 1248070
17.0%
fast_casual 933396
 
12.7%
bakery 305322
 
4.2%
brewery 280258
 
3.8%
fine_dining 247651
 
3.4%
fast_food 191842
 
2.6%
hotel 52400
 
0.7%
entertainment_complex 51919
 
0.7%
Other values (4) 39411
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 8574479
13.7%
I 8390326
13.4%
N 5962346
9.5%
F 5600247
 
8.9%
_ 3976343
 
6.4%
L 3569547
 
5.7%
Y 3117398
 
5.0%
D 2990301
 
4.8%
E 2838579
 
4.5%
G 2779469
 
4.4%
Other values (13) 14816717
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62615752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 8574479
13.7%
I 8390326
13.4%
N 5962346
9.5%
F 5600247
 
8.9%
_ 3976343
 
6.4%
L 3569547
 
5.7%
Y 3117398
 
5.0%
D 2990301
 
4.8%
E 2838579
 
4.5%
G 2779469
 
4.4%
Other values (13) 14816717
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62615752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 8574479
13.7%
I 8390326
13.4%
N 5962346
9.5%
F 5600247
 
8.9%
_ 3976343
 
6.4%
L 3569547
 
5.7%
Y 3117398
 
5.0%
D 2990301
 
4.8%
E 2838579
 
4.5%
G 2779469
 
4.4%
Other values (13) 14816717
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62615752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 8574479
13.7%
I 8390326
13.4%
N 5962346
9.5%
F 5600247
 
8.9%
_ 3976343
 
6.4%
L 3569547
 
5.7%
Y 3117398
 
5.0%
D 2990301
 
4.8%
E 2838579
 
4.5%
G 2779469
 
4.4%
Other values (13) 14816717
23.7%

city
Text

Distinct295
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size170.0 MiB
2025-02-12T18:37:20.214639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length20
Mean length8.2089334
Min length3

Characters and Unicode

Total characters60150196
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrand Forks
2nd rowGrand Forks
3rd rowGrand Forks
4th rowGrand Forks
5th rowGrand Forks
ValueCountFrequency (%)
toronto 1001138
 
12.0%
vancouver 602167
 
7.2%
ottawa 283547
 
3.4%
calgary 256330
 
3.1%
houston 189250
 
2.3%
edmonton 154532
 
1.8%
banff 137289
 
1.6%
mississauga 129973
 
1.6%
york 125956
 
1.5%
denver 121743
 
1.5%
Other values (316) 5372203
64.2%
2025-02-12T18:37:20.477953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 7565385
 
12.6%
a 5260106
 
8.7%
n 5003485
 
8.3%
r 4574222
 
7.6%
t 4010475
 
6.7%
e 3960850
 
6.6%
i 2872052
 
4.8%
l 2247121
 
3.7%
s 2115147
 
3.5%
u 1711412
 
2.8%
Other values (44) 20829941
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60150196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7565385
 
12.6%
a 5260106
 
8.7%
n 5003485
 
8.3%
r 4574222
 
7.6%
t 4010475
 
6.7%
e 3960850
 
6.6%
i 2872052
 
4.8%
l 2247121
 
3.7%
s 2115147
 
3.5%
u 1711412
 
2.8%
Other values (44) 20829941
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60150196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7565385
 
12.6%
a 5260106
 
8.7%
n 5003485
 
8.3%
r 4574222
 
7.6%
t 4010475
 
6.7%
e 3960850
 
6.6%
i 2872052
 
4.8%
l 2247121
 
3.7%
s 2115147
 
3.5%
u 1711412
 
2.8%
Other values (44) 20829941
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60150196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7565385
 
12.6%
a 5260106
 
8.7%
n 5003485
 
8.3%
r 4574222
 
7.6%
t 4010475
 
6.7%
e 3960850
 
6.6%
i 2872052
 
4.8%
l 2247121
 
3.7%
s 2115147
 
3.5%
u 1711412
 
2.8%
Other values (44) 20829941
34.6%

country
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
CA
4589801 
US
2737606 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14654814
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
CA 4589801
62.6%
US 2737606
37.4%

Length

2025-02-12T18:37:20.565153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T18:37:20.616413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ca 4589801
62.6%
us 2737606
37.4%

Most occurring characters

ValueCountFrequency (%)
C 4589801
31.3%
A 4589801
31.3%
U 2737606
18.7%
S 2737606
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14654814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 4589801
31.3%
A 4589801
31.3%
U 2737606
18.7%
S 2737606
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14654814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 4589801
31.3%
A 4589801
31.3%
U 2737606
18.7%
S 2737606
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14654814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 4589801
31.3%
A 4589801
31.3%
U 2737606
18.7%
S 2737606
18.7%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.8 MiB
00:00:00
4249611 
05:00:00
692339 
04:00:00
615708 
06:00:00
453733 
07:00:00
 
331455
Other values (13)
984561 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters58619256
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03:00:00
2nd row03:00:00
3rd row03:00:00
4th row03:00:00
5th row03:00:00

Common Values

ValueCountFrequency (%)
00:00:00 4249611
58.0%
05:00:00 692339
 
9.4%
04:00:00 615708
 
8.4%
06:00:00 453733
 
6.2%
07:00:00 331455
 
4.5%
08:00:00 198770
 
2.7%
03:00:00 187945
 
2.6%
09:00:00 144448
 
2.0%
02:00:00 116913
 
1.6%
10:00:00 112852
 
1.5%
Other values (8) 223633
 
3.1%

Length

2025-02-12T18:37:20.693870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 4249611
58.0%
05:00:00 692339
 
9.4%
04:00:00 615708
 
8.4%
06:00:00 453733
 
6.2%
07:00:00 331455
 
4.5%
08:00:00 198770
 
2.7%
03:00:00 187945
 
2.6%
09:00:00 144448
 
2.0%
02:00:00 116913
 
1.6%
10:00:00 112852
 
1.5%
Other values (8) 223633
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 40737456
69.5%
: 14654814
 
25.0%
5 711378
 
1.2%
4 622421
 
1.1%
6 460300
 
0.8%
1 369787
 
0.6%
7 351719
 
0.6%
3 200880
 
0.3%
8 198770
 
0.3%
2 167283
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58619256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40737456
69.5%
: 14654814
 
25.0%
5 711378
 
1.2%
4 622421
 
1.1%
6 460300
 
0.8%
1 369787
 
0.6%
7 351719
 
0.6%
3 200880
 
0.3%
8 198770
 
0.3%
2 167283
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58619256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40737456
69.5%
: 14654814
 
25.0%
5 711378
 
1.2%
4 622421
 
1.1%
6 460300
 
0.8%
1 369787
 
0.6%
7 351719
 
0.6%
3 200880
 
0.3%
8 198770
 
0.3%
2 167283
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58619256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40737456
69.5%
: 14654814
 
25.0%
5 711378
 
1.2%
4 622421
 
1.1%
6 460300
 
0.8%
1 369787
 
0.6%
7 351719
 
0.6%
3 200880
 
0.3%
8 198770
 
0.3%
2 167283
 
0.3%

Interactions

2025-02-12T18:35:06.741091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:17.280859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:35.524860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:53.288824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:10.969218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:28.665699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:45.816199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:03.356923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:22.211461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:37.133197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:51.988300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:08.928489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:19.018258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:37.082036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:54.854325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:12.479368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:30.207627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:47.395421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:05.096545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:23.959867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:38.224875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:53.158520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:10.442087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:20.689368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:38.697717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:56.364029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:14.115938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:31.756175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:49.028049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:06.772132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:25.622689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:39.439654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:54.433775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:11.718034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:22.350744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:40.290914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:57.946033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:15.676317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:33.333168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:50.665842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:08.390913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:27.119690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:40.906836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:56.029193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:12.978251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:23.963272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:41.907679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:59.564144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:17.232079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:34.800554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:52.243665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:09.949162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:28.386923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:42.695411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:57.741277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:14.156605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:25.557967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:43.459122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:01.124785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:18.829952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:36.314965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:53.812687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:11.830636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:30.023819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:44.273213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:59.039771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:15.227136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:27.323557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:45.014494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:02.676350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:20.450994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:37.892812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:55.413197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:13.565256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:31.103045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:45.769459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:00.553055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:16.802017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:29.122278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:46.642900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:04.427531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:22.190655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:39.476082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:57.092482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:15.248256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:32.165371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:47.085015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:01.680984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:18.049787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:30.645280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:48.210222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:06.017406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:23.783882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:41.079273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:58.486422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:17.001256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:33.237966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:48.265455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:02.959933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:19.190704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:32.284278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:49.989491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:07.751962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:25.448064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:42.689863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:00.165211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:18.828317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:34.495272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:49.433018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:04.282645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:20.293423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:33.997481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:32:51.651284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:09.383510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:27.099527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:33:44.330336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:01.767115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:20.515956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:36.026417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:34:50.706636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T18:35:05.459718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-02-12T18:35:20.645905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-12T18:35:30.867546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

bill_paid_at_localbill_total_billedbill_total_discount_item_levelbill_total_gratuitybill_total_netbill_total_taxbill_total_voidedbill_uuidbusiness_dateorder_duration_secondsorder_seated_at_localorder_closed_at_localorder_take_out_type_labelorder_uuidpayment_amountpayment_countpayment_total_tipsales_revenue_with_taxvenue_xref_idwaiter_uuidconceptcitycountrystart_of_day_offset
79432024-07-02 20:00:53102.530.00.095.07.530.0240702200053~8792ADCC-545E-4AF3-9836-9C428ED8285C2024-07-0250712024-07-02 18:36:222024-07-02 20:00:53dinein240702183622~4B0A3D27-501D-4C94-BEBC-7B7F3C91A0F4124.53122.00102.53885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288200421150750~37D0C51E-EC4F-4EA1-B549-D223DA183ABDBARGrand ForksUS03:00:00
79442024-07-03 21:43:395.385.00.05.00.380.0240703214339~B06B2A2F-B1A0-44C0-920A-242F41B58BD22024-07-0369682024-07-03 19:47:312024-07-03 21:43:39dinein240703194731~40651D6D-4A16-4889-8C12-0F9A00D8BCF75.3810.005.38885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79452024-07-03 18:23:56141.900.00.0132.09.900.0240703182356~D082FE98-841C-4EAB-B15E-BEA41FA1CDF02024-07-0320112024-07-03 17:50:252024-07-03 18:23:56dinein240703175025~0AEB61B0-220A-45A3-807E-78A1F432BB4C171.90130.00141.90885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79462024-07-03 19:33:2163.770.00.059.04.770.0240703193321~273CBB07-FAB0-49B7-A445-8474F9A4570F2024-07-0354802024-07-03 18:02:012024-07-03 19:33:21dinein240703180201~4B276496-BE92-4DE6-BD75-56592982701473.77110.0063.77885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79472024-07-05 21:01:14210.650.00.0195.015.650.0240705210114~D423B906-1AEE-4DB8-84BA-234267A14E052024-07-0547732024-07-05 19:41:412024-07-05 21:01:14dinein240705194141~F90FEB4F-D238-449E-B476-D16966DF4EEC252.65142.00210.65885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79482024-07-05 21:09:2650.930.00.047.03.930.0240705210926~CA81A307-A782-42D4-B81D-250556CBE8392024-07-0548202024-07-05 19:49:062024-07-05 21:09:26dinein240705194906~A090563D-5272-4F52-AD09-9316178D53F560.93110.0050.93885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79492024-07-05 20:21:48157.940.00.0146.011.940.0240705202148~A47EEC00-A5AF-45BA-A892-171E7CDEFE7C2024-07-0555552024-07-05 18:49:132024-07-05 20:21:48dinein240705184913~CEE4782F-A70E-4F8F-8912-BCFC97B5AD35187.94130.00157.94885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79502024-07-05 18:23:18151.580.00.0141.010.580.0240705182318~913C3F9F-E867-4E36-A3B1-E1E68FCBAD162024-07-0542632024-07-05 17:12:152024-07-05 18:23:18dinein240705171215~0EE5ACBB-3F73-4E16-A806-FF4084E46F5F181.58130.00151.58885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79512024-07-06 17:46:3279.550.00.074.05.550.0240706174632~1996E368-132E-43A2-9821-34BF48033CE12024-07-0619042024-07-06 17:14:482024-07-06 17:46:32dinein240706171448~3E583E38-3B31-45E7-A0BE-57C18EA214A779.5510.0079.55885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288220104154507~89085BB4-771B-40A1-9D20-B9745CC1DAFABARGrand ForksUS03:00:00
79522024-07-06 18:17:2256.640.00.052.54.140.0240706181722~A4851319-949C-4FF1-964D-C860684075B92024-07-0620772024-07-06 17:42:452024-07-06 18:17:22dinein240706174245~80595C36-1C6B-4842-88D1-B5BD7E063DE868.00111.3656.64885332b7f22a142e21b7459473003fddc17bfca5753ceb2f0f0d63cdf9de4288230816152907~E53E7ACE-FD5E-4E0B-B059-25E513F576B2BARGrand ForksUS03:00:00
bill_paid_at_localbill_total_billedbill_total_discount_item_levelbill_total_gratuitybill_total_netbill_total_taxbill_total_voidedbill_uuidbusiness_dateorder_duration_secondsorder_seated_at_localorder_closed_at_localorder_take_out_type_labelorder_uuidpayment_amountpayment_countpayment_total_tipsales_revenue_with_taxvenue_xref_idwaiter_uuidconceptcitycountrystart_of_day_offset
1671542024-12-27 17:40:5824.980.000.024.980.00.0241227174057~0A0B75D8-71B8-4625-80D8-91924BA9E18E2024-12-275072024-12-27 17:32:312024-12-27 17:40:58takeout241227173231~6B94AD74-4C11-4977-8A1D-2CD15C39AB3624.9810.024.9845f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817221116163611~F844A24E-44E8-40F2-B6D0-073FF805DBF7FAMILY_DININGEdmontonCA00:00:00
1671552024-12-27 17:54:1978.430.000.078.430.00.0241227175419~FDCA9A8B-8515-47A8-B438-E95FE1B7177B2024-12-2738532024-12-27 16:50:062024-12-27 17:54:19dinein241227165006~8559E840-33DD-47EA-844E-211E68ED17A378.4310.078.4345f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230202164435~D6FB6D1F-F2E2-4DB2-8278-59AAFF09A7F4FAMILY_DININGEdmontonCA00:00:00
1671562024-12-27 21:32:43108.900.000.0108.900.00.0241227213243~3465F212-5737-4B4A-9FE4-B528E630C08D2024-12-2738592024-12-27 20:28:242024-12-27 21:32:43dinein241227202824~BB60AF10-4446-4659-9F0C-B7E3942B62EA108.9010.0108.9045f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230202164435~D6FB6D1F-F2E2-4DB2-8278-59AAFF09A7F4FAMILY_DININGEdmontonCA00:00:00
1671572024-12-28 19:58:0075.420.000.075.420.00.0241228195800~416C102D-5E2B-47B7-9EA3-4D4D56367D962024-12-2830872024-12-28 19:06:332024-12-28 19:58:00dinein241228190633~A3D5144B-B4E1-4855-8718-B7995717B52675.4210.075.4245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6FAMILY_DININGEdmontonCA00:00:00
1671582024-12-28 20:38:2999.920.000.099.920.00.0241228203829~D10C3768-50E1-4E52-8F0A-F5CBA575A5142024-12-2874192024-12-28 18:34:502024-12-28 20:38:29dinein241228183450~DAE45042-47AA-4DF0-A580-4EB72ACB876399.9210.099.9245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6FAMILY_DININGEdmontonCA00:00:00
1671592024-12-29 20:32:5482.420.000.082.420.00.0241229203254~464AA06D-F986-4C51-8A53-A7B8362072D62024-12-2943822024-12-29 19:19:522024-12-29 20:32:54dinein241229191952~96F91DE2-5ECC-4AC9-9228-B0741F3494B182.4210.082.4245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817220831171122~1455DBA8-211C-4281-BF27-8698365CA3EBFAMILY_DININGEdmontonCA00:00:00
1671602024-12-30 20:36:1987.920.000.087.920.00.0241230203619~37348A86-98D1-4113-8EDD-06A3CF61A85B2024-12-3052442024-12-30 19:08:552024-12-30 20:36:19dinein241230190855~BB4604F2-B7BA-487A-99AF-7AEB77CA3EEC87.9210.087.9245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6FAMILY_DININGEdmontonCA00:00:00
1671612024-12-31 14:24:2351.960.000.051.960.00.0241231142423~9D2E9295-9E59-4E76-8242-D230C9FD24942024-12-319632024-12-31 14:08:202024-12-31 14:24:23takeout241231140820~4D531668-3D68-4789-A1DB-BE324D4583D551.9610.051.9645f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817161216233231~64B016BA-D9BA-49C7-8726-4BCB6516A11DFAMILY_DININGEdmontonCA00:00:00
1671622024-12-31 17:00:3449.3511.590.049.350.00.0241231170034~A9B4401D-26DC-4FC5-A4F9-0261D950DB3B2024-12-31592024-12-31 16:59:352024-12-31 17:00:34takeout241231165935~27989697-086C-4F7C-8AB2-8C0A8B2CF3A749.3510.049.3545f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6FAMILY_DININGEdmontonCA00:00:00
1671632024-12-31 18:55:0788.420.000.088.420.00.0241231185507~62C6632E-2957-4F0D-8AFF-CF89CA378FB42024-12-3165562024-12-31 17:05:512024-12-31 18:55:07dinein241231170551~4DEE5611-B193-40FB-858B-0650CE2F725988.4210.088.4245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6FAMILY_DININGEdmontonCA00:00:00